Let’s think about this; AI can spot the claims spikes after a Cat event, or even before the storm hits, pricing asset values in the path of a hurricane. Crunching data can reveal which particular make and model of car is popular with thieves, in a particualar town or city. Then there’s the study of health and mortality, digital patient records, health app data etc. all of which can assist Life insurers when it comes to pricing.
There are so many ways that AI and automated data processing can fight price inflation for insurance brands. As the recent increase in premiums for some pure EV cars shows, there is a pressing need for AI solutions, as consumers cannot afford 3K annual premiums indefinitely. Let’s get into it.
PREDICTIVE ANALYTICS: LONG TERM VALUE
Aaron Wright, FCAS, CPCU, Director of Strategy at Earnix, offers these thoughts:
A 2023 report revealed that 35% of C-suite executives believe that macroeconomic factors, including inflation, are the most significant trends driving change. AI helps insurers respond to market volatility, competitive shifts, and economic change, which is especially pertinent for insurers as they continue to face unprecedented changes in the market.
AI-driven enterprise rating and pricing engines allow insurers to instantly deploy pricing to online channels and serve up to millions of quotes and offers per day. With this sophistication, insurers can focus their pricing analysis on the inflationary cost drivers rather than taking a “one-size-fits-most” approach.
Predictive analytics can be used in scenarios which deviate from historical norms, however it is expected that these predictions will have greater than normal variance. This is true for not only larger inflation trends, but also localized spikes.
To address the variance and ensure automated pricing is effective against localized spikes in inflation, it is important to address three key items:
● Ensuring the input data in the models reflects the localized spikes
● Confirming the data and model refresh rate is at a level high enough to capture the speed at which the market shifts
● Maintaining a pricing tech stack that is agile enough to adjust to rapid changes
It’s important to also note that with the expected increase in variance comes an increased importance on business judgment to interpret models. It may take time to achieve your desired outcome, which is why appropriate feedback loops and an agile tech stack are important in enabling quick adjustments.
PRICING: CAN WE BUILD PERSONALISED QUOTES USING RPA?
Of course crunching tons of data into any platform will reveal various patterns on postcodes, age, gender, income, vehicles, commuting routes and a whole stack of other granular information. But the challenge surely has to be refining all those data points and adding weight to variable factors when it comes to pricing. That way insurers can sift the bigger Range Rover risks from the lesser ones. In short, what we are striving for is context on the data – what does it mean on an individual consumer level?
Tiago Cardoso, Principal Product Manager, Hyland, offers these thoughts;
“Automation and personalisation absolutely have to live separately. Automation is about replicating something what is already happening without depending on the original actors. In our case, it’s replicating what a human would decide and perform.
“Of course, the outcome of automation is that this thing can happen almost instantaneously – and at scale. So, if a person is able to do personalisation, an AI-driven automation will be able to do so as well. I think we’re at a point right now where we’re able to see AI-driven automation deliver for people.
“As large AI models become more widely available and become more capable of processing structured data (like logic and mathematics), they will become increasingly capable of perfectly understanding a specific quote. That requires contextualising it, reasoning the economics behind it, and then personalising its delivery.
“While this is something for the near future, these large models are already able to understand and use APIs that allow them to use tools like mathematical models, risk analysis programs, and others. Their capacity to factor in context from a wide range of sources, such as unstructured data, enables them to automate a personalised quote.
“Beyond that, these models can look into documentation from the customers and/or the insurance process; encode what this would mean in terms of the parameters of a quote; request existing tools data to build that quote; and then communicate this result to the customer – all in a way that explains that quote through the language of customer specificity and company policy.”
PERSONALISED QUOTES CAN BOOST CUSTOMER LOYALTY
Deploying automated systems and personalising quotes isn’t just about fighting inflation. There is another long term benefit for insurers worth considering, as Daniel Derham, (below) Insurance Specialist, SAS UK & Ireland explains;
“AI & machine learning are a win, win for everybody in insurance. The use cases are really widespread.
“Starting with customers. Insurers should be able to offer the right product and policy to their customers so they are covered for what they need at a competitive price. Previously, the market’s approach was ‘here’s our product, this is what it contains and this is what you get – take it or leave it’. But now customers expect their individual needs to be met – an expectation largely accelerated by how the pandemic changed personal circumstances. If someone was suddenly at home five days a week and not in the office, they might think ‘why should I pay for car insurance covering 10,000 miles a year?’ They’d want their insurer to be accommodating.
“By using cloud analytics and AI to analyse big data sets, it helps insurers understand their customers and their risk profiles much better. It gives them confidence that they are charging the right premium associated with underwriting a specific product and the relevant risks for the needs of that individual customer. The right risk for the right price.
“This is what ultimately can make an insurer’s business more sustainable. They can offer tailored policies to improve customer satisfaction, retention and longevity – discouraging them from shopping around.
“These same customer datasets of activity, when analysed on a huge scale through AI, is also enabling insurers to combat fraud – by ultimately helping them to spot patterns of fraudulent behaviour and prevent fraudulent claims being processed. There are many insurers who are using or considering AI models to do this, given that fraudulent claims continue to rise.
“Looking internally, AI helps insurers to forecast resources much more efficiently. If for example an insurer has a full call centre Tuesday to Thursday but data shows that actually most calls are coming in Monday and Friday, they can plan better and optimise shift patterns so lots of agents aren’t sat at desks when call volumes are low.
“As in many sectors, the customer used to be two or three steps removed from what’s good for an insurer’s business. Now everything is led by customer demand and because of that competitiveness, insurers are having to use every tool at their disposal to offer a better product and service. Those who aren’t leveraging AI are already getting left behind.”
REGTECH CAN BUILD PRODUCTS FASTER, AND LAUNCH THEM QUICKER
Saving time means saving money. When it comes to compliance it also means avoiding fines levied by local regulators. It sounds obvious, but the shorter the development time on new insurance products, then the more competitive they will be on price in the market. That’s another potential win. So humdrum stuff like compliance is another area where AI and automation can make a big difference.
Regsearch is a Luxembourg-based startup specialising in AI-driven solutions for regulatory compliance and risk management in the financial services industry. Their flagship product, ‘Regi,’ leverages advanced AI algorithms to streamline compliance processes and enhance decision-making for businesses.
Following a demo-day event in January 2023, plus a series of workshops with the regulator, wider government, and corporates, the REGI user interface was designed – demonstrating the Isle of Man’s open and collaborative approach to innovation. This system links stakeholders, so that everyone from an insurtech start-up to the local regulator knows that regulatory compliance is hardwired in.
Commenting, Ros Lynch, Head of Supervisory Practices and Innovation Strategy at Isle of Man Financial Services Authority said:
“We are delighted to collaborate with Regsearch to leverage AI capabilities for the benefit of the Isle of Man’s business community. This collaboration underscores our commitment to embracing innovative technologies for our stakeholders to use and in turn assist in strengthening our regulatory standards”.
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